{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据读取与预处理\n",
    "plt.style.use('ggplot')  #使用ggplot风格\n",
    "na_values=['N/A']  #缺失值类型为N/A\n",
    "df=pd.read_csv(r\"C:\\Users\\yangbirong\\Desktop\\vgsales.csv\",na_values=na_values)\n",
    "df=df.dropna(how='any',axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原数据集共16598条数据\n",
      "原数据集中缺失值共329条\n",
      "现数据集共有16291条数据\n"
     ]
    }
   ],
   "source": [
    "# 计算原数据集中的缺失值数量\n",
    "df_old = pd.read_csv(r\"C:\\Users\\yangbirong\\Desktop\\vgsales.csv\")\n",
    "print('原数据集共{}条数据'.format(len(df_old)))\n",
    "missing_values_count = df_old.isnull().sum().sum()\n",
    "print('原数据集中缺失值共{}条'.format(missing_values_count))\n",
    "# 删除包含缺失值的行\n",
    "df_new = df_old.dropna(how='any', axis=0)\n",
    "print('现数据集共有{}条数据'.format(len(df_new)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "新数据集中没有缺失值\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 如果你想确认新数据集中没有缺失值，可以检查：\n",
    "if df_new.isnull().values.any():\n",
    "    print(\"新数据集中仍然存在缺失值\")\n",
    "else:\n",
    "    print(\"新数据集中没有缺失值\")\n",
    "df.to_csv('game_sales_cleaned.csv', index=False)\n",
    "#异常值\n",
    "df=pd.read_csv('game_sales_cleaned.csv')\n",
    "#北美洲销量箱体图异常值预测\n",
    "plt.boxplot(df['NA_Sales'], vert=False)\n",
    "plt.title('NA_Sales Distribution')\n",
    "plt.show()\n",
    "#欧洲销量箱体图异常值预测\n",
    "plt.boxplot(df['EU_Sales'], vert=False)\n",
    "plt.title('EU_Sales Distribution')\n",
    "plt.show()\n",
    "#日本销量箱体图异常值预测\n",
    "plt.boxplot(df['JP_Sales'], vert=False)\n",
    "plt.title('JP_Sales Distribution')\n",
    "plt.show()\n",
    "#其他地区销量箱体图异常值预测\n",
    "plt.boxplot(df['Other_Sales'], vert=False)\n",
    "plt.title('Other_Sales Distribution')\n",
    "plt.show()\n",
    "#全球销量箱体图异常值预测\n",
    "plt.boxplot(df['Global_Sales'], vert=False)\n",
    "plt.title('Global_Sales Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=pd.read_csv('game_sales_cleaned.csv')\n",
    "mean_sales = data['NA_Sales'].mean()\n",
    "# NA使用平均值填充异常值\n",
    "data['NA_Sales'] = data['NA_Sales'].apply(lambda x: mean_sales if x > mean_sales + 1.5 * data['NA_Sales'].std() or x < mean_sales - 1.5 * data['NA_Sales'].std() else x)\n",
    "plt.boxplot(data['NA_Sales'], vert=False)\n",
    "plt.title('2-NA_Sales Distribution ')\n",
    "plt.show()\n",
    "#EU使用平均值填充异常值\n",
    "data['EU_Sales'] = data['EU_Sales'].apply(lambda x: mean_sales if x > mean_sales + 1.5 * data['EU_Sales'].std() or x < mean_sales - 1.5 * data['EU_Sales'].std() else x)\n",
    "plt.boxplot(data['EU_Sales'], vert=False)\n",
    "plt.title('2-EU_Sales Distribution ')\n",
    "plt.show()\n",
    "#JP使用平均值填充异常值\n",
    "data['JP_Sales'] = data['JP_Sales'].apply(lambda x: mean_sales if x > mean_sales + 1.5 * data['JP_Sales'].std() or x < mean_sales - 1.5 * data['JP_Sales'].std() else x)\n",
    "plt.boxplot(data['JP_Sales'], vert=False)\n",
    "plt.title('2-JP_Sales Distribution ')\n",
    "plt.show()\n",
    "#Other使用平均值填充异常值\n",
    "data['Other_Sales'] = data['Other_Sales'].apply(lambda x: mean_sales if x > mean_sales + 1.5 * data['Other_Sales'].std() or x < mean_sales - 1.5 * data['Other_Sales'].std() else x)\n",
    "plt.boxplot(data['Other_Sales'], vert=False)\n",
    "plt.title('2-Other_Sales Distribution ')\n",
    "plt.show()\n",
    "#Global使用平均值填充异常值\n",
    "data['Global_Sales'] = data['Global_Sales'].apply(lambda x: mean_sales if x > mean_sales + 1.5 * data['Global_Sales'].std() or x < mean_sales - 1.5 * data['Global_Sales'].std() else x)\n",
    "plt.boxplot(data['Global_Sales'], vert=False)\n",
    "plt.title('2-Global_Sales Distribution ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16291 entries, 0 to 16290\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Rank          16291 non-null  int64  \n",
      " 1   Name          16291 non-null  object \n",
      " 2   Platform      16291 non-null  object \n",
      " 3   Year          16291 non-null  int64  \n",
      " 4   Genre         16291 non-null  object \n",
      " 5   Publisher     16291 non-null  object \n",
      " 6   NA_Sales      16291 non-null  float64\n",
      " 7   EU_Sales      16291 non-null  float64\n",
      " 8   JP_Sales      16291 non-null  float64\n",
      " 9   Other_Sales   16291 non-null  float64\n",
      " 10  Global_Sales  16291 non-null  float64\n",
      "dtypes: float64(5), int64(2), object(4)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.to_csv('1-game_sales_cleaned.csv', index=False)\n",
    "#数据集成\n",
    "df=pd.read_csv('1-game_sales_cleaned.csv')\n",
    "df=df.duplicated().sum()  #识别重复值\n",
    "print(df) #查看是否有重复值\n",
    "#查询到没有重复值\n",
    "\n",
    "#数据转换\n",
    "df=pd.read_csv('1-game_sales_cleaned.csv')\n",
    "df['Year']=pd.to_numeric(df['Year'],errors='coerce').fillna(0).astype(np.int64)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NA正样本数（销量高于中位数）：7831\n",
      "NA负样本数（销量低于或等于中位数）：8460\n",
      "NA正样本比例：48.07%\n",
      "NA负样本比例：51.93%\n",
      "EU正样本数（销量高于中位数）：7955\n",
      "EU负样本数（销量低于或等于中位数）：8336\n",
      "EU正样本比例：48.83%\n",
      "EU负样本比例：51.17%\n",
      "JP正样本数（销量高于中位数）：6084\n",
      "JP负样本数（销量低于或等于中位数）：10207\n",
      "JP正样本比例：37.35%\n",
      "JP负样本比例：62.65%\n",
      "Other正样本数（销量高于中位数）：6578\n",
      "Other负样本数（销量低于或等于中位数）：9713\n",
      "Other正样本比例：40.38%\n",
      "Other负样本比例：59.62%\n",
      "Global正样本数（销量高于中位数）：8062\n",
      "Global负样本数（销量低于或等于中位数）：8229\n",
      "Global正样本比例：49.49%\n",
      "Global负样本比例：50.51%\n"
     ]
    }
   ],
   "source": [
    "#正负样本数处理\n",
    "\n",
    "#计算中位数\n",
    "median_sales = df['NA_Sales'].median()\n",
    "#以中位数为分界线，定义销量高于中位数的为正样本，低于或等于中位数的为负样本\n",
    "df['NA_median'] = (df['NA_Sales'] > median_sales).astype(int)\n",
    "# 计算正负样本比例\n",
    "positive_count = df['NA_median'].sum()\n",
    "negative_count = len(df) - positive_count\n",
    "positive_ratio = positive_count / len(df)\n",
    "negative_ratio = negative_count / len(df)\n",
    "print(f\"NA正样本数（销量高于中位数）：{positive_count}\")\n",
    "print(f\"NA负样本数（销量低于或等于中位数）：{negative_count}\")\n",
    "print(f\"NA正样本比例：{positive_ratio:.2%}\")\n",
    "print(f\"NA负样本比例：{negative_ratio:.2%}\")\n",
    "\n",
    "#计算中位数\n",
    "median2_sales = df['EU_Sales'].median()\n",
    "#以中位数为分界线，定义销量高于中位数的为正样本，低于或等于中位数的为负样本\n",
    "df['EU_median'] = (df['EU_Sales'] > median2_sales).astype(int)\n",
    "# 计算正负样本比例\n",
    "positive_count = df['EU_median'].sum()\n",
    "negative_count = len(df) - positive_count\n",
    "positive_ratio = positive_count / len(df)\n",
    "negative_ratio = negative_count / len(df)\n",
    "print(f\"EU正样本数（销量高于中位数）：{positive_count}\")\n",
    "print(f\"EU负样本数（销量低于或等于中位数）：{negative_count}\")\n",
    "print(f\"EU正样本比例：{positive_ratio:.2%}\")\n",
    "print(f\"EU负样本比例：{negative_ratio:.2%}\")\n",
    "\n",
    "#计算中位数\n",
    "median3_sales = df['JP_Sales'].median()\n",
    "#以中位数为分界线，定义销量高于中位数的为正样本，低于或等于中位数的为负样本\n",
    "df['JP_median'] = (df['JP_Sales'] > median3_sales).astype(int)\n",
    "# 计算正负样本比例\n",
    "positive_count = df['JP_median'].sum()\n",
    "negative_count = len(df) - positive_count\n",
    "positive_ratio = positive_count / len(df)\n",
    "negative_ratio = negative_count / len(df)\n",
    "print(f\"JP正样本数（销量高于中位数）：{positive_count}\")\n",
    "print(f\"JP负样本数（销量低于或等于中位数）：{negative_count}\")\n",
    "print(f\"JP正样本比例：{positive_ratio:.2%}\")\n",
    "print(f\"JP负样本比例：{negative_ratio:.2%}\")\n",
    "\n",
    "#计算中位数\n",
    "median4_sales = df['Other_Sales'].median()\n",
    "#以中位数为分界线，定义销量高于中位数的为正样本，低于或等于中位数的为负样本\n",
    "df['Other_median'] = (df['Other_Sales'] > median4_sales).astype(int)\n",
    "# 计算正负样本比例\n",
    "positive_count = df['Other_median'].sum()\n",
    "negative_count = len(df) - positive_count\n",
    "positive_ratio = positive_count / len(df)\n",
    "negative_ratio = negative_count / len(df)\n",
    "print(f\"Other正样本数（销量高于中位数）：{positive_count}\")\n",
    "print(f\"Other负样本数（销量低于或等于中位数）：{negative_count}\")\n",
    "print(f\"Other正样本比例：{positive_ratio:.2%}\")\n",
    "print(f\"Other负样本比例：{negative_ratio:.2%}\")\n",
    "\n",
    "#计算中位数\n",
    "median5_sales = df['Global_Sales'].median()\n",
    "#以中位数为分界线，定义销量高于中位数的为正样本，低于或等于中位数的为负样本\n",
    "df['Global_median'] = (df['Global_Sales'] > median5_sales).astype(int)\n",
    "# 计算正负样本比例\n",
    "positive_count = df['Global_median'].sum()\n",
    "negative_count = len(df) - positive_count\n",
    "positive_ratio = positive_count / len(df)\n",
    "negative_ratio = negative_count / len(df)\n",
    "print(f\"Global正样本数（销量高于中位数）：{positive_count}\")\n",
    "print(f\"Global负样本数（销量低于或等于中位数）：{negative_count}\")\n",
    "print(f\"Global正样本比例：{positive_ratio:.2%}\")\n",
    "print(f\"Global负样本比例：{negative_ratio:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       NA_Sales  NA_Sales_minmax  NA_Sales_zscore\n",
      "0      0.265647         0.178286         0.360377\n",
      "1      0.265647         0.178286         0.360377\n",
      "2      0.265647         0.178286         0.360377\n",
      "3      0.265647         0.178286         0.360377\n",
      "4      0.265647         0.178286         0.360377\n",
      "...         ...              ...              ...\n",
      "16286  0.010000         0.006711        -0.644599\n",
      "16287  0.010000         0.006711        -0.644599\n",
      "16288  0.000000         0.000000        -0.683910\n",
      "16289  0.000000         0.000000        -0.683910\n",
      "16290  0.010000         0.006711        -0.644599\n",
      "\n",
      "[16291 rows x 3 columns]\n",
      "       EU_Sales  EU_Sales_minmax  EU_Sales_zscore\n",
      "0      0.265647         0.260438         1.051421\n",
      "1      0.265647         0.260438         1.051421\n",
      "2      0.265647         0.260438         1.051421\n",
      "3      0.265647         0.260438         1.051421\n",
      "4      0.265647         0.260438         1.051421\n",
      "...         ...              ...              ...\n",
      "16286  0.000000         0.000000        -0.580467\n",
      "16287  0.000000         0.000000        -0.580467\n",
      "16288  0.000000         0.000000        -0.580467\n",
      "16289  0.010000         0.009804        -0.519036\n",
      "16290  0.000000         0.000000        -0.580467\n",
      "\n",
      "[16291 rows x 3 columns]\n",
      "       JP_Sales  JP_Sales_minmax  JP_Sales_zscore\n",
      "0      0.265647           0.3639         2.027858\n",
      "1      0.265647           0.3639         2.027858\n",
      "2      0.265647           0.3639         2.027858\n",
      "3      0.265647           0.3639         2.027858\n",
      "4      0.265647           0.3639         2.027858\n",
      "...         ...              ...              ...\n",
      "16286  0.000000           0.0000        -0.451485\n",
      "16287  0.000000           0.0000        -0.451485\n",
      "16288  0.000000           0.0000        -0.451485\n",
      "16289  0.000000           0.0000        -0.451485\n",
      "16290  0.000000           0.0000        -0.451485\n",
      "\n",
      "[16291 rows x 3 columns]\n",
      "       Other_Sales  Other_Sales_minmax  Other_Sales_zscore\n",
      "0         0.265647            0.482994            3.177553\n",
      "1         0.265647            0.482994            3.177553\n",
      "2         0.265647            0.482994            3.177553\n",
      "3         0.265647            0.482994            3.177553\n",
      "4         0.265647            0.482994            3.177553\n",
      "...            ...                 ...                 ...\n",
      "16286     0.000000            0.000000           -0.514965\n",
      "16287     0.000000            0.000000           -0.514965\n",
      "16288     0.000000            0.000000           -0.514965\n",
      "16289     0.000000            0.000000           -0.514965\n",
      "16290     0.000000            0.000000           -0.514965\n",
      "\n",
      "[16291 rows x 3 columns]\n",
      "       Global_Sales  Global_Sales_minmax  Global_Sales_zscore\n",
      "0          0.265647             0.098326            -0.169358\n",
      "1          0.265647             0.098326            -0.169358\n",
      "2          0.265647             0.098326            -0.169358\n",
      "3          0.265647             0.098326            -0.169358\n",
      "4          0.265647             0.098326            -0.169358\n",
      "...             ...                  ...                  ...\n",
      "16286      0.010000             0.000000            -0.736455\n",
      "16287      0.010000             0.000000            -0.736455\n",
      "16288      0.010000             0.000000            -0.736455\n",
      "16289      0.010000             0.000000            -0.736455\n",
      "16290      0.010000             0.000000            -0.736455\n",
      "\n",
      "[16291 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "#数据集特征处理\n",
    "# NA最小-最大归一化\n",
    "df['NA_Sales_minmax'] = (df['NA_Sales'] - df['NA_Sales'].min()) / (df['NA_Sales'].max() - df['NA_Sales'].min())\n",
    "# NA-Z-score标准化\n",
    "df['NA_Sales_zscore'] = (df['NA_Sales'] - df['NA_Sales'].mean()) / df['NA_Sales'].std()\n",
    "print(df[['NA_Sales', 'NA_Sales_minmax', 'NA_Sales_zscore']])\n",
    "\n",
    "#EU最小-最大归一化\n",
    "df['EU_Sales_minmax'] = (df['EU_Sales'] - df['EU_Sales'].min()) / (df['EU_Sales'].max() - df['EU_Sales'].min())\n",
    "# Z-score标准化\n",
    "df['EU_Sales_zscore'] = (df['EU_Sales'] - df['EU_Sales'].mean()) / df['EU_Sales'].std()\n",
    "print(df[['EU_Sales', 'EU_Sales_minmax', 'EU_Sales_zscore']])\n",
    "\n",
    "#JP最小-最大归一化\n",
    "df['JP_Sales_minmax'] = (df['JP_Sales'] - df['JP_Sales'].min()) / (df['JP_Sales'].max() - df['JP_Sales'].min())\n",
    "# Z-score标准化\n",
    "df['JP_Sales_zscore'] = (df['JP_Sales'] - df['JP_Sales'].mean()) / df['JP_Sales'].std()\n",
    "print(df[['JP_Sales', 'JP_Sales_minmax', 'JP_Sales_zscore']])\n",
    "\n",
    "#Other最小-最大归一化\n",
    "df['Other_Sales_minmax'] = (df['Other_Sales'] - df['Other_Sales'].min()) / (df['Other_Sales'].max() - df['Other_Sales'].min())\n",
    "# Z-score标准化\n",
    "df['Other_Sales_zscore'] = (df['Other_Sales'] - df['Other_Sales'].mean()) / df['Other_Sales'].std()\n",
    "print(df[['Other_Sales', 'Other_Sales_minmax', 'Other_Sales_zscore']])\n",
    "\n",
    "#Global最小-最大归一化\n",
    "df['Global_Sales_minmax'] = (df['Global_Sales'] - df['Global_Sales'].min()) / (df['Global_Sales'].max() - df['Global_Sales'].min())\n",
    "# Z-score标准化\n",
    "df['Global_Sales_zscore'] = (df['Global_Sales'] - df['Global_Sales'].mean()) / df['Global_Sales'].std()\n",
    "print(df[['Global_Sales', 'Global_Sales_minmax', 'Global_Sales_zscore']])\n",
    "df.to_csv('2-game_sales_cleaned.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1404cdd3670>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#导入必要的库\n",
    "import numpy as np  \n",
    "from sklearn.cluster import DBSCAN   #从scikit-learn库中导入DBSCAN聚类算法\n",
    "from sklearn.datasets import make_moons   #生成半月形数据集的函数\n",
    "from sklearn.preprocessing import StandardScaler  \n",
    "import matplotlib.pyplot as plt  \n",
    "  \n",
    "# 生成示例数据集（两个半圆形的聚类）  \n",
    "X, y = make_moons(n_samples=200, noise=0.05, random_state=0)  \n",
    "  \n",
    "# 数据标准化  \n",
    "scaler = StandardScaler()  \n",
    "X = scaler.fit_transform(X)  \n",
    "  \n",
    "# 使用DBSCAN进行聚类（这里我们手动选择参数，但在实际应用中你可能需要使用网格搜索）  \n",
    "dbscan = DBSCAN(eps=0.3, min_samples=5)  \n",
    "dbscan.fit(X)  \n",
    "labels = dbscan.labels_  \n",
    "  \n",
    "# 可视化聚类结果  \n",
    "plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', marker='o') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# 标记噪声点（通常为-1）  \n",
    "unique_labels = set(labels)  \n",
    "colors = [plt.cm.viridis(each) for each in np.linspace(0, 1, len(unique_labels))]  \n",
    "for k, col in zip(unique_labels, colors):  \n",
    "    if k == -1:  \n",
    "        # 黑色用于噪声点  \n",
    "        col = [0, 0, 0, 1]  \n",
    "  \n",
    "    class_member_mask = (labels == k)  \n",
    "  \n",
    "    xy = X[class_member_mask]  \n",
    "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),  \n",
    "             markeredgecolor='k', markersize=14)  \n",
    "  \n",
    "plt.title('DBSCAN Clustering')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Rank                      Name Platform  Year         Genre Publisher  \\\n",
      "0     1                Wii Sports      Wii  2006        Sports  Nintendo   \n",
      "1     2         Super Mario Bros.      NES  1985      Platform  Nintendo   \n",
      "2     3            Mario Kart Wii      Wii  2008        Racing  Nintendo   \n",
      "3     4         Wii Sports Resort      Wii  2009        Sports  Nintendo   \n",
      "4     5  Pokemon Red/Pokemon Blue       GB  1996  Role-Playing  Nintendo   \n",
      "\n",
      "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  ...  NA_Sales_minmax  \\\n",
      "0  0.265647  0.265647  0.265647     0.265647  ...         0.178286   \n",
      "1  0.265647  0.265647  0.265647     0.265647  ...         0.178286   \n",
      "2  0.265647  0.265647  0.265647     0.265647  ...         0.178286   \n",
      "3  0.265647  0.265647  0.265647     0.265647  ...         0.178286   \n",
      "4  0.265647  0.265647  0.265647     0.265647  ...         0.178286   \n",
      "\n",
      "   NA_Sales_zscore  EU_Sales_minmax  EU_Sales_zscore  JP_Sales_minmax  \\\n",
      "0         0.360377         0.260438         1.051421           0.3639   \n",
      "1         0.360377         0.260438         1.051421           0.3639   \n",
      "2         0.360377         0.260438         1.051421           0.3639   \n",
      "3         0.360377         0.260438         1.051421           0.3639   \n",
      "4         0.360377         0.260438         1.051421           0.3639   \n",
      "\n",
      "   JP_Sales_zscore  Other_Sales_minmax  Other_Sales_zscore  \\\n",
      "0         2.027858            0.482994            3.177553   \n",
      "1         2.027858            0.482994            3.177553   \n",
      "2         2.027858            0.482994            3.177553   \n",
      "3         2.027858            0.482994            3.177553   \n",
      "4         2.027858            0.482994            3.177553   \n",
      "\n",
      "   Global_Sales_minmax  Global_Sales_zscore  \n",
      "0             0.098326            -0.169358  \n",
      "1             0.098326            -0.169358  \n",
      "2             0.098326            -0.169358  \n",
      "3             0.098326            -0.169358  \n",
      "4             0.098326            -0.169358  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "#预处理后的数据加载\n",
    "import pandas as pd  \n",
    "  \n",
    "# CSV文件名为'games_sales.csv'，并且位于当前工作目录下  \n",
    "df = pd.read_csv('2-game_sales_cleaned.csv')  \n",
    "  \n",
    "# 显示前几行数据以确认加载正确  \n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据再处理\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder  \n",
    "from sklearn.compose import ColumnTransformer  \n",
    "  \n",
    "# 选择数值型特征  \n",
    "numeric_features = ['Year', 'NA_Sales', 'EU_Sales', 'JP_Sales', 'Other_Sales']  \n",
    "  \n",
    "# 选择分类特征  \n",
    "categorical_features = ['Platform', 'Genre']  \n",
    "  \n",
    "# 创建ColumnTransformer进行预处理  \n",
    "preprocessor = ColumnTransformer(  \n",
    "    transformers=[  \n",
    "        ('num', StandardScaler(), numeric_features),  \n",
    "        ('cat', OneHotEncoder(handle_unknown='ignore', sparse=False), categorical_features),  \n",
    "    ]  \n",
    ")  \n",
    "  \n",
    "# 拟合并转换数据  \n",
    "X = preprocessor.fit_transform(df)  \n",
    "y = df['Global_Sales']  \n",
    "  \n",
    "# 注意：X现在是一个NumPy数组，包含了标准化后的数值特征和独热编码后的分类特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       Name  Cluster\n",
      "0                Wii Sports        0\n",
      "1         Super Mario Bros.        1\n",
      "2            Mario Kart Wii        2\n",
      "3         Wii Sports Resort        3\n",
      "4  Pokemon Red/Pokemon Blue        4\n"
     ]
    }
   ],
   "source": [
    "#归一化后的DBSCAN聚类\n",
    "from sklearn.cluster import DBSCAN  \n",
    "  \n",
    "# 仅对数值特征进行聚类（作为示例）  \n",
    "X_num = df[numeric_features].values  \n",
    "  \n",
    "# 使用DBSCAN进行聚类（需要调整eps和min_samples参数）  \n",
    "dbscan = DBSCAN(eps=0.5, min_samples=5)  # 这些参数是示例，需要根据实际数据进行调整  \n",
    "labels = dbscan.fit_predict(X_num)  \n",
    "  \n",
    "# 将聚类标签添加回原始数据框（可选）  \n",
    "df['Cluster'] = labels  \n",
    "  \n",
    "# 显示带有聚类标签的前几行数据  \n",
    "print(df[['Name', 'Cluster']].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预处理后的数据集的预测: [0.53600562 0.06084732 0.1683506  ... 0.11682484 0.11837539 0.20243071]\n",
      "前五个预测值： 0.5360056155295805, 0.06084731618478317, 0.1683505950031392, 0.6437141377856788, 0.2531309833988342\n"
     ]
    }
   ],
   "source": [
    "# 划分训练集和测试集  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  \n",
    "  \n",
    "# 创建并训练模型  \n",
    "model = LinearRegression()  \n",
    "model.fit(X_train, y_train)  \n",
    "  \n",
    "# 进行预测  \n",
    "y_pred = model.predict(X_test)  \n",
    "  \n",
    "# 输出预测值  \n",
    "print(\"预处理后的数据集的预测:\",y_pred)  \n",
    "# 提取前五个测试样本的预测值  \n",
    "y_pred_first_five = y_pred[:5] \n",
    "print(\"前五个预测值：\",', '.join(map(str, y_pred_first_five)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中等销量 低销量 低销量 高销量 低销量\n"
     ]
    }
   ],
   "source": [
    "# 预测出来的数据，即数字值列表  \n",
    "predictions = [0.5360056155295805, 0.06084731618478317, 0.1683505950031392, 0.6437141377856788, 0.2531309833988342]\n",
    "  \n",
    "# 定义阈值和对应的文字标签  \n",
    "thresholds = [0, 0.3, 0.6, 1]  # 假设的阈值列表  \n",
    "labels = ['低销量', '中等销量', '高销量', '非常高销量']  # 对应的文字标签  \n",
    "  \n",
    "# 将预测的数字值转换为对应的文字标签的函数  \n",
    "def convert_to_label(prediction, thresholds, labels):  \n",
    "    # 找到预测值所属的区间，并返回对应的标签  \n",
    "    for i in range(len(thresholds) - 1):  \n",
    "        if thresholds[i] <= prediction < thresholds[i+1]:  \n",
    "            return labels[i]  \n",
    "    # 如果预测值大于或等于最后一个阈值，返回最后一个标签  \n",
    "    return labels[-1]  \n",
    "  \n",
    "# 应用函数到预测值列表，得到文字标签列表  \n",
    "label_predictions = [convert_to_label(pred, thresholds, labels) for pred in predictions]  \n",
    "  \n",
    "# 将文字标签列表转换为用空格隔开的字符串  \n",
    "label_string = ' '.join(label_predictions)  \n",
    "  \n",
    "# 输出结果  \n",
    "print(label_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.05739563961978029\n",
      "均方根误差: 0.23957387090369495\n"
     ]
    }
   ],
   "source": [
    "#模型训练与评估\n",
    "from sklearn.linear_model import LinearRegression  \n",
    "from sklearn.model_selection import train_test_split  \n",
    "from sklearn.metrics import mean_squared_error  \n",
    "  \n",
    "# 划分训练集和测试集  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  \n",
    "  \n",
    "# 创建并训练模型  \n",
    "model = LinearRegression()  \n",
    "model.fit(X_train, y_train)  \n",
    "  \n",
    "# 进行预测  \n",
    "y_pred = model.predict(X_test)  \n",
    "  \n",
    "# 评估模型  \n",
    "mse = mean_squared_error(y_test, y_pred)  \n",
    "rmse = mse ** 0.5  # 计算均方根误差（RMSE）  \n",
    "print(f'均方误差: {mse}')  #MSE的值越小，表示模型的预测结果越接近真实值，模型性能越好\n",
    "print(f'均方根误差: {rmse}')  #RMSE值越小，表示模型的预测精度越高。由于RMSE计算的是误差的平方根，因此它对大的误差非常敏感，这有助于模型在训练过程中更关注那些预测偏差较大的样本。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#原数据数据加载\n",
    "import pandas as pd \n",
    "import pandas as pd  \n",
    "from sklearn.linear_model import LinearRegression  \n",
    "from sklearn.model_selection import train_test_split   \n",
    "  \n",
    "# 假设你的CSV文件名为'games_sales.csv'，并且位于当前工作目录下  \n",
    "df = pd.read_csv('C:/Users/yangbirong/Desktop/vgsales.csv')  \n",
    "  \n",
    "# 显示前几行数据以确认加载正确  \n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 选择特征和目标变量  \n",
    "X = df[['NA_Sales', 'EU_Sales', 'JP_Sales', 'Other_Sales']]  # 数值型特征  \n",
    "y = df['Global_Sales']  # 目标变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 在分割数据集之前保存前五个样本  \n",
    "X_first_five_original = X.head(5)  \n",
    "y_first_five_original = y.head(5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分割数据集  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) \n",
    "# 创建并训练模型  \n",
    "model = LinearRegression()  \n",
    "model.fit(X_train, y_train)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "前五个样本的预测值:\n",
      "82.72850892788962, 40.23702571130934, 35.825710585171095, 32.99609135051143, 31.378206640799867\n"
     ]
    }
   ],
   "source": [
    "# 使用训练好的模型来预测之前保存的前五个样本  \n",
    "X_first_five = X_first_five_original.copy()  # 确保不修改原始数据  \n",
    "y_pred_first_five = model.predict(X_first_five)  \n",
    "  \n",
    "# 输出预测值  \n",
    "print(\"前五个样本的预测值:\")  \n",
    "print(', '.join(map(str, y_pred_first_five)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "高销量 中等销量 中等销量 中等销量 中等销量\n"
     ]
    }
   ],
   "source": [
    "# 预测出来的数据，即数字值列表  \n",
    "predictions = [82.72850892788962, 40.23702571130934, 35.825710585171095, 32.99609135051143, 31.378206640799867]\n",
    "  \n",
    "# 定义阈值和对应的文字标签  \n",
    "thresholds = [0, 30, 60, 100]  # 假设的阈值列表  \n",
    "labels = ['低销量', '中等销量', '高销量', '非常高销量']  # 对应的文字标签  \n",
    "  \n",
    "# 将预测的数字值转换为对应的文字标签的函数  \n",
    "def convert_to_label(prediction, thresholds, labels):  \n",
    "    # 找到预测值所属的区间，并返回对应的标签  \n",
    "    for i in range(len(thresholds) - 1):  \n",
    "        if thresholds[i] <= prediction < thresholds[i+1]:  \n",
    "            return labels[i]  \n",
    "    # 如果预测值大于或等于最后一个阈值，返回最后一个标签  \n",
    "    return labels[-1]  \n",
    "  \n",
    "# 应用函数到预测值列表，得到文字标签列表  \n",
    "label_predictions = [convert_to_label(pred, thresholds, labels) for pred in predictions]  \n",
    "  \n",
    "# 将文字标签列表转换为用空格隔开的字符串  \n",
    "label_string = ' '.join(label_predictions)  \n",
    "  \n",
    "# 输出结果  \n",
    "print(label_string)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv",
   "language": "python",
   "name": "myenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
